knitr::opts_chunk$set(echo = TRUE, warning = FALSE, comment = FALSE, message = FALSE,
cache = FALSE)
library(tidyverse)
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library(xgboost)
##
## Attaching package: 'xgboost'
##
## The following object is masked from 'package:dplyr':
##
## slice
library(Metrics)
library(ggpmisc)
## Loading required package: ggpp
##
## Attaching package: 'ggpp'
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## The following object is masked from 'package:ggplot2':
##
## annotate
library(ggthemes)
match_dir = 'data/matchups/'
model_dir = 'data/models/'
# Set random seed
set.seed(799)
The purpose of this script is to apply the xgboost
algorithm to Remote Sensing Imagery of Lake Yojoa in Honduras, to
estimate Yojoa water clarity. You can read more about this lake here.
In this ‘more paranoid’ approach, we use a train-test-validate method, where the validation is holdout data from the train-test to confirm model performance.
#list all the files in the match directory
match = list.files(match_dir)
prepData = function(df) {
#make a rowid column
df_prep = df %>%
rowid_to_column() %>%
mutate(secchi = as.numeric(secchi)) %>% #there's one wonky value in here with two decimal points... dropping from this analysis
filter(!is.na(secchi))
}
#load the matchup files
threeDay = read.csv(file.path(match_dir, match[grepl('three', match) & !grepl('us', match)])) %>%
prepData(.)
fiveDay = read.csv(file.path(match_dir, match[grepl('five', match) & !grepl('us', match)])) %>%
prepData(.)
multiMatch = read.csv(file.path(match_dir, match[grepl('multi', match)])) %>%
prepData(.)
We want to predict the secchi value in these datasets,
so let’s set the target as that variable:
## Identify our target (value is secchi)
target <- 'secchi'
For each dataset, let’s grab 60% of the data as the ‘train’ set, and split the remainder between ‘test’ and ‘val’.
##Pull 60% as training data
train_3 <- threeDay %>%
sample_frac(0.6)
test_3 <- threeDay %>%
filter(!rowid %in% train_3$rowid) %>%
sample_frac(0.5)
val_3 <- threeDay %>%
filter(!rowid %in% test_3$rowid) %>%
filter(!rowid %in% train_3$rowid)
##Pull 60% as training data
train_5 <- fiveDay %>%
sample_frac(0.6)
test_5 <- fiveDay %>%
filter(!rowid %in% train_5$rowid) %>%
sample_frac(0.5)
val_5 <-fiveDay %>%
filter(!rowid %in% test_5$rowid) %>%
filter(!rowid %in% train_5$rowid)
The ‘local knowledge’ window creates a variable matchup window, where matchups can be within 7 days in all times of year except October and November, when the lake can have dramatic shifts in clarity in a short period of time.
train_j <- multiMatch %>%
sample_frac(0.6)
test_j <- multiMatch %>%
filter(!rowid %in% train_j$rowid) %>%
sample_frac(0.5)
val_j <- multiMatch %>%
filter(!rowid %in% test_j$rowid) %>%
filter(!rowid %in% train_j$rowid)
Here, we indicate the features to be used in our models. We’ll use the visual bands and add in summaries of ERA5 met data. In our datasets, the 5-day met summaries have the suffix ‘_5’, etc. The first two models only include recent weather summaries, but the final two models include 5 or 7 day weather summaries as well as the previous day’s weather.
band_met3_feats <- c('med_Blue_corr', 'med_Green_corr', 'med_Red_corr', 'med_Nir_corr',
'RN', 'BG', 'RB','GB',
'tot_sol_rad_KJpm2_3', 'max_temp_degK_3', 'mean_temp_degK_3', 'min_temp_degK_3',
'tot_precip_m_3', 'mean_wind_mps_3')
band_met5_feats <- c('med_Blue_corr', 'med_Green_corr', 'med_Red_corr', 'med_Nir_corr',
'RN', 'BG', 'RB','GB',
'tot_sol_rad_KJpm2_5', 'max_temp_degK_5', 'mean_temp_degK_5', 'min_temp_degK_5',
'tot_precip_m_5', 'mean_wind_mps_5')
band_met51_feats <- c('med_Blue_corr', 'med_Green_corr', 'med_Red_corr', 'med_Nir_corr',
'RN', 'BG', 'RB','GB',
'tot_sol_rad_KJpm2_5', 'max_temp_degK_5', 'mean_temp_degK_5', 'min_temp_degK_5',
'tot_precip_m_5', 'mean_wind_mps_5',
'solar_rad_KJpm2_prev', 'precip_m_prev','air_temp_degK_prev','wind_speed_mps_prev')
band_met71_feats <- c('med_Blue_corr', 'med_Green_corr', 'med_Red_corr', 'med_Nir_corr',
'RN', 'BG', 'RB','GB',
'tot_sol_rad_KJpm2_7', 'max_temp_degK_7', 'mean_temp_degK_7', 'min_temp_degK_7',
'tot_precip_m_7', 'mean_wind_mps_7',
'solar_rad_KJpm2_prev', 'precip_m_prev','air_temp_degK_prev','wind_speed_mps_prev')
## 3 day window, 3 days previous met
dtrain_3d_3m <- xgb.DMatrix(data = as.matrix(train_3[,band_met3_feats]),
label = train_3[,target])
dtest_3d_3m <- xgb.DMatrix(data = as.matrix(test_3[,band_met3_feats]),
label = test_3[,target])
dval_3d_3m <- xgb.DMatrix(data = as.matrix(val_3[,band_met3_feats]),
label = val_3[,target])
## 3 day window, 5 days previous met
dtrain_3d_5m <- xgb.DMatrix(data = as.matrix(train_3[,band_met5_feats]),
label = train_3[,target])
dtest_3d_5m <- xgb.DMatrix(data = as.matrix(test_3[,band_met5_feats]),
label = test_3[,target])
dval_3d_5m <- xgb.DMatrix(data = as.matrix(val_3[,band_met5_feats]),
label = val_3[,target])
## 3 day window, 5/1 days previous met
dtrain_3d_51m <- xgb.DMatrix(data = as.matrix(train_3[,band_met51_feats]),
label = train_3[,target])
dtest_3d_51m <- xgb.DMatrix(data = as.matrix(test_3[,band_met51_feats]),
label = test_3[,target])
dval_3d_51m <- xgb.DMatrix(data = as.matrix(val_3[,band_met51_feats]),
label = val_3[,target])
## 3 day window, 7/1 days previous met
dtrain_3d_71m <- xgb.DMatrix(data = as.matrix(train_3[,band_met71_feats]),
label = train_3[,target])
dtest_3d_71m <- xgb.DMatrix(data = as.matrix(test_3[,band_met71_feats]),
label = test_3[,target])
dval_3d_71m <- xgb.DMatrix(data = as.matrix(val_3[,band_met71_feats]),
label = val_3[,target])
## 5 day window, 3 days previous met
dtrain_5d_3m <- xgb.DMatrix(data = as.matrix(train_5[,band_met3_feats]),
label = train_5[,target])
dtest_5d_3m <- xgb.DMatrix(data = as.matrix(test_5[,band_met3_feats]),
label = test_5[,target])
dval_5d_3m <- xgb.DMatrix(data = as.matrix(val_5[,band_met3_feats]),
label = val_5[,target])
## 5 day window, 5 days previous met
dtrain_5d_5m <- xgb.DMatrix(data = as.matrix(train_5[,band_met5_feats]),
label = train_5[,target])
dtest_5d_5m <- xgb.DMatrix(data = as.matrix(test_5[,band_met5_feats]),
label = test_5[,target])
dval_5d_5m <- xgb.DMatrix(data = as.matrix(val_5[,band_met5_feats]),
label = val_5[,target])
## 5 day window, 5/1 days previous met
dtrain_5d_51m <- xgb.DMatrix(data = as.matrix(train_5[,band_met51_feats]),
label = train_5[,target])
dtest_5d_51m <- xgb.DMatrix(data = as.matrix(test_5[,band_met51_feats]),
label = test_5[,target])
dval_5d_51m <- xgb.DMatrix(data = as.matrix(val_5[,band_met51_feats]),
label = val_5[,target])
## 5 day window, 7/1 days previous met
dtrain_5d_71m <- xgb.DMatrix(data = as.matrix(train_5[,band_met71_feats]),
label = train_5[,target])
dtest_5d_71m <- xgb.DMatrix(data = as.matrix(test_5[,band_met71_feats]),
label = test_5[,target])
dval_5d_71m <- xgb.DMatrix(data = as.matrix(val_5[,band_met71_feats]),
label = val_5[,target])
## local knowledge window, 5 days previous met
dtrain_jd_5m <- xgb.DMatrix(data = as.matrix(train_j[,band_met5_feats]),
label = train_j[,target])
dtest_jd_5m <- xgb.DMatrix(data = as.matrix(test_j[,band_met5_feats]),
label = test_j[,target])
dval_jd_5m <- xgb.DMatrix(data = as.matrix(val_j[,band_met5_feats]),
label = val_j[,target])
## local knowledge window, 3 days previous met
dtrain_jd_3m <- xgb.DMatrix(data = as.matrix(train_j[,band_met3_feats]),
label = train_j[,target])
dtest_jd_3m <- xgb.DMatrix(data = as.matrix(test_j[,band_met3_feats]),
label = test_j[,target])
dval_jd_3m <- xgb.DMatrix(data = as.matrix(val_j[,band_met3_feats]),
label = val_j[,target])
## local knowledge window, 5/1 days previous met
dtrain_jd_51m <- xgb.DMatrix(data = as.matrix(train_j[,band_met51_feats]),
label = train_j[,target])
dtest_jd_51m <- xgb.DMatrix(data = as.matrix(test_j[,band_met51_feats]),
label = test_j[,target])
dval_jd_51m <- xgb.DMatrix(data = as.matrix(val_j[,band_met51_feats]),
label = val_j[,target])
## local knowledge window, 7/1 days previous met
dtrain_jd_71m <- xgb.DMatrix(data = as.matrix(train_j[,band_met71_feats]),
label = train_j[,target])
dtest_jd_71m <- xgb.DMatrix(data = as.matrix(test_j[,band_met71_feats]),
label = test_j[,target])
dval_jd_71m <- xgb.DMatrix(data = as.matrix(val_j[,band_met71_feats]),
label = val_j[,target])
This is an xgboost optimization method developed by Sam Sillen where you list many possible hyperparameter options and then create a matrix of all possible combinations - aka ‘grid search’ - and grab the top 20 performing combinations of hyperparameters by square error (our loss statistic).
grid_train <- expand.grid(
max_depth= c(3,6,8),
subsample = c(.5,.8,1),
colsample_bytree= c(.5,.8,1),
eta = c(0.1, 0.3),
min_child_weight= c(3,5,7)
)
hypertune_xgboost = function(train, test, grid){
params <- list(booster = "gbtree", objective = 'reg:squarederror',
eta=grid$eta ,max_depth=grid$max_depth,
min_child_weight=grid$min_child_weight,
subsample=grid$subsample,
colsample_bytree=grid$colsample_bytree)
xgb.naive <- xgb.train(params = params, data = train, nrounds = 1000,
watchlist = list(train = train, val = test),
verbose = 0,
early_stopping_rounds = 20)
summary <- grid %>% mutate(val_loss = xgb.naive$best_score, best_message = xgb.naive$best_msg,
mod = list(xgb.naive))
return(summary)
}
Note, evaluation is turned off for these chunks as to not overwrite previous models and parameter tuning in next section.
## Hypertune xgboost 3 day window, 3 day met
xgboost_hypertune_3d_3m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_3d_3m,dtest_3d_3m,current)
})
mod_summary_3d_3m <- xgboost_hypertune_3d_3m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_3d_3m <- xgboost_hypertune_3d_3m[xgboost_hypertune_3d_3m$val_loss==min(xgboost_hypertune_3d_3m$val_loss),]
save(mod_summary_3d_3m,best_mod_3d_3m, file = 'data/models/paramsxg_mp_val_3d_3m.RData')
## Hypertune xgboost 3 day window, 5 day met
xgboost_hypertune_3d_5m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_3d_5m,dtest_3d_5m,current)
})
mod_summary_3d_5m <- xgboost_hypertune_3d_5m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_3d_5m <- xgboost_hypertune_3d_5m[xgboost_hypertune_3d_5m$val_loss==min(xgboost_hypertune_3d_5m$val_loss),]
save(mod_summary_3d_5m,best_mod_3d_5m, file = 'data/models/paramsxg_mp_val_3d_5m.RData')
## Hypertune xgboost 5 day window, 5/1 day met
xgboost_hypertune_3d_51m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_3d_51m,dtest_3d_51m,current)
})
mod_summary_3d_51m <- xgboost_hypertune_3d_51m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_3d_51m <- xgboost_hypertune_3d_51m[xgboost_hypertune_3d_51m$val_loss==min(xgboost_hypertune_3d_51m$val_loss),]
save(mod_summary_3d_51m,best_mod_3d_51m, file = 'data/models/paramsxg_mp_val_3d_51m.RData')
## Hypertune xgboost 5 day window, 7/1 day met
xgboost_hypertune_3d_71m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_3d_71m,dtest_3d_71m,current)
})
mod_summary_3d_71m <- xgboost_hypertune_3d_71m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_3d_71m <- xgboost_hypertune_3d_71m[xgboost_hypertune_3d_71m$val_loss==min(xgboost_hypertune_3d_71m$val_loss),]
save(mod_summary_3d_71m,best_mod_3d_71m, file = 'data/models/paramsxg_mp_val_3d_71m.RData')
## Hypertune xgboost 5 day window, 3 day met
xgboost_hypertune_5d_3m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_5d_3m,dtest_5d_3m,current)
})
mod_summary_5d_3m <- xgboost_hypertune_5d_3m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_5d_3m <- xgboost_hypertune_5d_3m[xgboost_hypertune_5d_3m$val_loss==min(xgboost_hypertune_5d_3m$val_loss),]
save(mod_summary_5d_3m,best_mod_5d_3m, file = 'data/models/paramsxg_mp_val_5d_3m.RData')
## Hypertune xgboost 5 day window, 5 day met
xgboost_hypertune_5d_5m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_5d_5m,dtest_5d_5m,current)
})
mod_summary_5d_5m <- xgboost_hypertune_5d_5m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_5d_5m <- xgboost_hypertune_5d_5m[xgboost_hypertune_5d_5m$val_loss==min(xgboost_hypertune_5d_5m$val_loss),]
save(mod_summary_5d_5m,best_mod_5d_5m, file = 'data/models/paramsxg_mp_val_5d_5m.RData')
## Hypertune xgboost 5 day window, 5/1 day met
xgboost_hypertune_5d_51m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_5d_51m,dtest_5d_51m,current)
})
mod_summary_5d_51m <- xgboost_hypertune_5d_51m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_5d_51m <- xgboost_hypertune_5d_51m[xgboost_hypertune_5d_51m$val_loss==min(xgboost_hypertune_5d_51m$val_loss),]
save(mod_summary_5d_51m,best_mod_5d_51m, file = 'data/models/paramsxg_mp_val_5d_51m.RData')
## Hypertune xgboost 5 day window, 7/1 day met
xgboost_hypertune_5d_71m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_5d_71m,dtest_5d_71m,current)
})
mod_summary_5d_71m <- xgboost_hypertune_5d_71m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_5d_71m <- xgboost_hypertune_5d_71m[xgboost_hypertune_5d_71m$val_loss==min(xgboost_hypertune_5d_71m$val_loss),]
save(mod_summary_5d_71m,best_mod_5d_71m, file = 'data/models/paramsxg_mp_val_5d_71m.RData')
## Hypertune xgboost local knowledge window, 3 day met
xgboost_hypertune_jd_3m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_jd_3m,dtest_jd_3m,current)
})
mod_summary_jd_3m <- xgboost_hypertune_jd_3m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_jd_3m <- xgboost_hypertune_jd_3m[xgboost_hypertune_jd_3m$val_loss==min(xgboost_hypertune_jd_3m$val_loss),]
save(mod_summary_jd_3m,best_mod_jd_3m, file = 'data/models/paramsxg_mp_val_jd_3m.RData')
## Hypertune xgboost local knowledge window, 5 day met
xgboost_hypertune_jd_5m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_jd_5m,dtest_jd_5m,current)
})
mod_summary_jd_5m <- xgboost_hypertune_jd_5m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_jd_5m <- xgboost_hypertune_jd_5m[xgboost_hypertune_jd_5m$val_loss==min(xgboost_hypertune_jd_5m$val_loss),]
save(mod_summary_jd_5m,best_mod_jd_5m, file = 'data/models/paramsxg_mp_val_jd_5m.RData')
## Hypertune xgboost local knowledge window, 5/1 day met
xgboost_hypertune_jd_51m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_jd_51m,dtest_jd_51m,current)
})
mod_summary_jd_51m <- xgboost_hypertune_jd_51m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_jd_51m <- xgboost_hypertune_jd_51m[xgboost_hypertune_jd_51m$val_loss==min(xgboost_hypertune_jd_51m$val_loss),]
save(mod_summary_jd_51m,best_mod_jd_51m, file = 'data/models/paramsxg_mp_val_jd_51m.RData')
## Hypertune xgboost local knowledge window, 7/1 day met
xgboost_hypertune_jd_71m <- grid_train %>%
pmap_dfr(function(...) {
current <- tibble(...)
hypertune_xgboost(dtrain_jd_71m,dtest_jd_71m,current)
})
mod_summary_jd_71m <- xgboost_hypertune_jd_71m %>%
arrange(val_loss) %>%
dplyr::slice(1:20)
best_mod_jd_71m <- xgboost_hypertune_jd_71m[xgboost_hypertune_jd_71m$val_loss==min(xgboost_hypertune_jd_71m$val_loss),]
save(mod_summary_jd_71m,best_mod_jd_71m, file = 'data/models/paramsxg_mp_val_jd_71m.RData')
load('data/models/paramsxg_mp_val_3d_3m.RData')
load('data/models/paramsxg_mp_val_3d_5m.RData')
load('data/models/paramsxg_mp_val_3d_71m.RData')
load('data/models/paramsxg_mp_val_3d_51m.RData')
load('data/models/paramsxg_mp_val_5d_3m.RData')
load('data/models/paramsxg_mp_val_5d_5m.RData')
load('data/models/paramsxg_mp_val_5d_51m.RData')
load('data/models/paramsxg_mp_val_5d_71m.RData')
load('data/models/paramsxg_mp_val_jd_3m.RData')
load('data/models/paramsxg_mp_val_jd_5m.RData')
load('data/models/paramsxg_mp_val_jd_51m.RData')
load('data/models/paramsxg_mp_val_jd_71m.RData')
Now that these are loaded, we need to look at the test/train statistics. Ideally the train/test RMSE are relatively close so we don’t choose too overfit of a model. Below, we apply the model to the validation dataset and plot the validation observed versus predicted.
mod_summary_3d_3m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[103]\ttrain-rmse:0.113883\tval-rmse:0.624023"
FALSE 2 "[114]\ttrain-rmse:0.075017\tval-rmse:0.629418"
FALSE 3 "[41]\ttrain-rmse:0.507325\tval-rmse:0.629930"
FALSE 4 "[11]\ttrain-rmse:0.357180\tval-rmse:0.645146"
FALSE 5 "[48]\ttrain-rmse:0.465745\tval-rmse:0.646048"
FALSE 6 "[86]\ttrain-rmse:0.154512\tval-rmse:0.647932"
FALSE 7 "[13]\ttrain-rmse:0.361831\tval-rmse:0.652924"
FALSE 8 "[13]\ttrain-rmse:0.278694\tval-rmse:0.662542"
FALSE 9 "[95]\ttrain-rmse:0.265339\tval-rmse:0.668107"
FALSE 10 "[21]\ttrain-rmse:0.459728\tval-rmse:0.673707"
FALSE 11 "[28]\ttrain-rmse:0.173449\tval-rmse:0.674939"
FALSE 12 "[49]\ttrain-rmse:0.247211\tval-rmse:0.677989"
FALSE 13 "[80]\ttrain-rmse:0.288392\tval-rmse:0.684942"
FALSE 14 "[20]\ttrain-rmse:0.528480\tval-rmse:0.686146"
FALSE 15 "[14]\ttrain-rmse:0.653506\tval-rmse:0.686244"
FALSE 16 "[102]\ttrain-rmse:0.416878\tval-rmse:0.686613"
FALSE 17 "[173]\ttrain-rmse:0.054351\tval-rmse:0.689267"
FALSE 18 "[47]\ttrain-rmse:0.608462\tval-rmse:0.691494"
FALSE 19 "[59]\ttrain-rmse:0.355369\tval-rmse:0.694319"
FALSE 20 "[70]\ttrain-rmse:0.246263\tval-rmse:0.694387"
mod_summary_3d_5m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[45]\ttrain-rmse:0.251663\tval-rmse:0.643949"
FALSE 2 "[12]\ttrain-rmse:0.516066\tval-rmse:0.663720"
FALSE 3 "[74]\ttrain-rmse:0.194844\tval-rmse:0.664838"
FALSE 4 "[15]\ttrain-rmse:0.251613\tval-rmse:0.667447"
FALSE 5 "[14]\ttrain-rmse:0.702113\tval-rmse:0.673156"
FALSE 6 "[40]\ttrain-rmse:0.473091\tval-rmse:0.676498"
FALSE 7 "[13]\ttrain-rmse:0.701906\tval-rmse:0.677969"
FALSE 8 "[44]\ttrain-rmse:0.453686\tval-rmse:0.681272"
FALSE 9 "[81]\ttrain-rmse:0.360825\tval-rmse:0.684850"
FALSE 10 "[40]\ttrain-rmse:0.329602\tval-rmse:0.685379"
FALSE 11 "[26]\ttrain-rmse:0.394861\tval-rmse:0.686575"
FALSE 12 "[40]\ttrain-rmse:0.640284\tval-rmse:0.686996"
FALSE 13 "[14]\ttrain-rmse:0.491823\tval-rmse:0.687216"
FALSE 14 "[45]\ttrain-rmse:0.307773\tval-rmse:0.687521"
FALSE 15 "[27]\ttrain-rmse:0.515921\tval-rmse:0.691605"
FALSE 16 "[73]\ttrain-rmse:0.451104\tval-rmse:0.692233"
FALSE 17 "[10]\ttrain-rmse:0.653044\tval-rmse:0.695878"
FALSE 18 "[7]\ttrain-rmse:0.621689\tval-rmse:0.696761"
FALSE 19 "[12]\ttrain-rmse:0.358257\tval-rmse:0.697600"
FALSE 20 "[37]\ttrain-rmse:0.374047\tval-rmse:0.699012"
mod_summary_3d_51m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[63]\ttrain-rmse:0.523729\tval-rmse:0.584816"
FALSE 2 "[91]\ttrain-rmse:0.289679\tval-rmse:0.587800"
FALSE 3 "[10]\ttrain-rmse:0.578118\tval-rmse:0.590249"
FALSE 4 "[48]\ttrain-rmse:0.427781\tval-rmse:0.593098"
FALSE 5 "[161]\ttrain-rmse:0.219855\tval-rmse:0.599805"
FALSE 6 "[40]\ttrain-rmse:0.077712\tval-rmse:0.603377"
FALSE 7 "[42]\ttrain-rmse:0.331187\tval-rmse:0.603583"
FALSE 8 "[53]\ttrain-rmse:0.247760\tval-rmse:0.603798"
FALSE 9 "[101]\ttrain-rmse:0.124181\tval-rmse:0.611221"
FALSE 10 "[25]\ttrain-rmse:0.478237\tval-rmse:0.612739"
FALSE 11 "[119]\ttrain-rmse:0.084907\tval-rmse:0.615967"
FALSE 12 "[91]\ttrain-rmse:0.111615\tval-rmse:0.616458"
FALSE 13 "[76]\ttrain-rmse:0.140558\tval-rmse:0.617523"
FALSE 14 "[56]\ttrain-rmse:0.321637\tval-rmse:0.619190"
FALSE 15 "[109]\ttrain-rmse:0.247061\tval-rmse:0.623077"
FALSE 16 "[19]\ttrain-rmse:0.386529\tval-rmse:0.625079"
FALSE 17 "[38]\ttrain-rmse:0.603413\tval-rmse:0.626733"
FALSE 18 "[56]\ttrain-rmse:0.179758\tval-rmse:0.627747"
FALSE 19 "[45]\ttrain-rmse:0.520056\tval-rmse:0.628916"
FALSE 20 "[106]\ttrain-rmse:0.085112\tval-rmse:0.629479"
mod_summary_3d_71m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[120]\ttrain-rmse:0.178971\tval-rmse:0.546375"
FALSE 2 "[87]\ttrain-rmse:0.119423\tval-rmse:0.584757"
FALSE 3 "[58]\ttrain-rmse:0.519666\tval-rmse:0.588536"
FALSE 4 "[25]\ttrain-rmse:0.173121\tval-rmse:0.591312"
FALSE 5 "[143]\ttrain-rmse:0.260337\tval-rmse:0.600821"
FALSE 6 "[52]\ttrain-rmse:0.428538\tval-rmse:0.604501"
FALSE 7 "[63]\ttrain-rmse:0.015292\tval-rmse:0.604834"
FALSE 8 "[62]\ttrain-rmse:0.239200\tval-rmse:0.613092"
FALSE 9 "[26]\ttrain-rmse:0.169868\tval-rmse:0.615110"
FALSE 10 "[10]\ttrain-rmse:0.493345\tval-rmse:0.616362"
FALSE 11 "[26]\ttrain-rmse:0.487033\tval-rmse:0.616373"
FALSE 12 "[92]\ttrain-rmse:0.483514\tval-rmse:0.616810"
FALSE 13 "[69]\ttrain-rmse:0.090567\tval-rmse:0.617584"
FALSE 14 "[50]\ttrain-rmse:0.386077\tval-rmse:0.618442"
FALSE 15 "[53]\ttrain-rmse:0.541363\tval-rmse:0.618449"
FALSE 16 "[31]\ttrain-rmse:0.405210\tval-rmse:0.618792"
FALSE 17 "[96]\ttrain-rmse:0.149206\tval-rmse:0.622800"
FALSE 18 "[173]\ttrain-rmse:0.205208\tval-rmse:0.624057"
FALSE 19 "[28]\ttrain-rmse:0.412852\tval-rmse:0.624704"
FALSE 20 "[128]\ttrain-rmse:0.180527\tval-rmse:0.625379"
# most of best models are overfit, so looking for train/test RMSE that are closer
optimized_booster_3d_3m <- mod_summary_3d_3m$mod[3][[1]]
optimized_booster_3d_5m <- mod_summary_3d_5m$mod[2][[1]]
optimized_booster_3d_51m <- mod_summary_3d_51m$mod[1][[1]]
optimized_booster_3d_71m <- mod_summary_3d_71m$mod[3][[1]]
# Apply best mod
preds_3 <- val_3 %>%
mutate(pred_secchi_3d_5m = predict(optimized_booster_3d_5m, dval_3d_5m),
pred_secchi_3d_3m = predict(optimized_booster_3d_3m, dval_3d_3m),
pred_secchi_3d_51m = predict(optimized_booster_3d_51m, dval_3d_51m),
pred_secchi_3d_71m = predict(optimized_booster_3d_71m, dval_3d_71m))
evals_3 <- preds_3 %>%
summarise(across(c(pred_secchi_3d_5m, pred_secchi_3d_3m, pred_secchi_3d_51m, pred_secchi_3d_71m),
list(rmse = ~rmse(secchi, .),
mae = ~mae(secchi, .),
mape = ~mape(secchi, .),
bias = ~bias(secchi, .),
p.bias = ~percent_bias(secchi, .),
smape = ~smape(secchi, .),
r2 = ~cor(secchi, .)^2),
.names = "{fn}_{col}"))
evals_3
FALSE rmse_pred_secchi_3d_5m mae_pred_secchi_3d_5m mape_pred_secchi_3d_5m
FALSE 1 0.817311 0.5896351 0.1730654
FALSE bias_pred_secchi_3d_5m p.bias_pred_secchi_3d_5m smape_pred_secchi_3d_5m
FALSE 1 0.176719 0.008944264 0.1769119
FALSE r2_pred_secchi_3d_5m rmse_pred_secchi_3d_3m mae_pred_secchi_3d_3m
FALSE 1 0.3490189 0.8109347 0.5731821
FALSE mape_pred_secchi_3d_3m bias_pred_secchi_3d_3m p.bias_pred_secchi_3d_3m
FALSE 1 0.1657093 0.2063319 0.02247587
FALSE smape_pred_secchi_3d_3m r2_pred_secchi_3d_3m rmse_pred_secchi_3d_51m
FALSE 1 0.1698005 0.3737727 0.7157991
FALSE mae_pred_secchi_3d_51m mape_pred_secchi_3d_51m bias_pred_secchi_3d_51m
FALSE 1 0.5126511 0.148163 0.1459944
FALSE p.bias_pred_secchi_3d_51m smape_pred_secchi_3d_51m r2_pred_secchi_3d_51m
FALSE 1 0.01299 0.1521153 0.499015
FALSE rmse_pred_secchi_3d_71m mae_pred_secchi_3d_71m mape_pred_secchi_3d_71m
FALSE 1 0.7106818 0.52801 0.1531263
FALSE bias_pred_secchi_3d_71m p.bias_pred_secchi_3d_71m smape_pred_secchi_3d_71m
FALSE 1 0.1631748 0.0195518 0.1572277
FALSE r2_pred_secchi_3d_71m
FALSE 1 0.508388
mod_summary_5d_3m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[11]\ttrain-rmse:0.327546\tval-rmse:0.980860"
FALSE 2 "[21]\ttrain-rmse:0.299935\tval-rmse:0.993849"
FALSE 3 "[24]\ttrain-rmse:0.334483\tval-rmse:0.996286"
FALSE 4 "[9]\ttrain-rmse:0.540934\tval-rmse:1.011602"
FALSE 5 "[39]\ttrain-rmse:0.307835\tval-rmse:1.013431"
FALSE 6 "[31]\ttrain-rmse:0.434795\tval-rmse:1.019757"
FALSE 7 "[14]\ttrain-rmse:0.411971\tval-rmse:1.025668"
FALSE 8 "[56]\ttrain-rmse:0.310286\tval-rmse:1.039395"
FALSE 9 "[10]\ttrain-rmse:0.256525\tval-rmse:1.040997"
FALSE 10 "[10]\ttrain-rmse:0.545060\tval-rmse:1.042106"
FALSE 11 "[21]\ttrain-rmse:0.236715\tval-rmse:1.044129"
FALSE 12 "[13]\ttrain-rmse:0.571564\tval-rmse:1.046111"
FALSE 13 "[33]\ttrain-rmse:0.358365\tval-rmse:1.048972"
FALSE 14 "[54]\ttrain-rmse:0.159286\tval-rmse:1.049130"
FALSE 15 "[26]\ttrain-rmse:0.234417\tval-rmse:1.050759"
FALSE 16 "[15]\ttrain-rmse:0.401728\tval-rmse:1.050817"
FALSE 17 "[40]\ttrain-rmse:0.397549\tval-rmse:1.053423"
FALSE 18 "[38]\ttrain-rmse:0.233800\tval-rmse:1.054601"
FALSE 19 "[41]\ttrain-rmse:0.451990\tval-rmse:1.054910"
FALSE 20 "[38]\ttrain-rmse:0.481745\tval-rmse:1.056298"
mod_summary_5d_5m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[19]\ttrain-rmse:0.319591\tval-rmse:0.952580"
FALSE 2 "[20]\ttrain-rmse:0.437341\tval-rmse:0.976179"
FALSE 3 "[22]\ttrain-rmse:0.284349\tval-rmse:0.993413"
FALSE 4 "[33]\ttrain-rmse:0.054067\tval-rmse:1.003331"
FALSE 5 "[35]\ttrain-rmse:0.360349\tval-rmse:1.004167"
FALSE 6 "[14]\ttrain-rmse:0.263795\tval-rmse:1.008768"
FALSE 7 "[21]\ttrain-rmse:0.403337\tval-rmse:1.008797"
FALSE 8 "[39]\ttrain-rmse:0.381127\tval-rmse:1.010368"
FALSE 9 "[12]\ttrain-rmse:0.400475\tval-rmse:1.011368"
FALSE 10 "[38]\ttrain-rmse:0.151418\tval-rmse:1.012041"
FALSE 11 "[14]\ttrain-rmse:0.429832\tval-rmse:1.012421"
FALSE 12 "[15]\ttrain-rmse:0.460666\tval-rmse:1.013479"
FALSE 13 "[56]\ttrain-rmse:0.388745\tval-rmse:1.013859"
FALSE 14 "[52]\ttrain-rmse:0.316780\tval-rmse:1.014898"
FALSE 15 "[18]\ttrain-rmse:0.302212\tval-rmse:1.016566"
FALSE 16 "[11]\ttrain-rmse:0.258037\tval-rmse:1.018300"
FALSE 17 "[69]\ttrain-rmse:0.337460\tval-rmse:1.019363"
FALSE 18 "[64]\ttrain-rmse:0.382138\tval-rmse:1.021017"
FALSE 19 "[39]\ttrain-rmse:0.063766\tval-rmse:1.021045"
FALSE 20 "[68]\ttrain-rmse:0.358500\tval-rmse:1.021180"
mod_summary_5d_51m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[37]\ttrain-rmse:0.170404\tval-rmse:0.939598"
FALSE 2 "[16]\ttrain-rmse:0.227196\tval-rmse:0.977729"
FALSE 3 "[14]\ttrain-rmse:0.476502\tval-rmse:0.977982"
FALSE 4 "[18]\ttrain-rmse:0.246417\tval-rmse:0.979007"
FALSE 5 "[36]\ttrain-rmse:0.245908\tval-rmse:0.981467"
FALSE 6 "[18]\ttrain-rmse:0.408781\tval-rmse:0.992688"
FALSE 7 "[46]\ttrain-rmse:0.152733\tval-rmse:0.994134"
FALSE 8 "[10]\ttrain-rmse:0.323117\tval-rmse:0.995858"
FALSE 9 "[19]\ttrain-rmse:0.355387\tval-rmse:0.996048"
FALSE 10 "[25]\ttrain-rmse:0.233858\tval-rmse:1.007492"
FALSE 11 "[52]\ttrain-rmse:0.377163\tval-rmse:1.010191"
FALSE 12 "[68]\ttrain-rmse:0.111663\tval-rmse:1.015421"
FALSE 13 "[21]\ttrain-rmse:0.383587\tval-rmse:1.016748"
FALSE 14 "[54]\ttrain-rmse:0.267656\tval-rmse:1.016777"
FALSE 15 "[66]\ttrain-rmse:0.250886\tval-rmse:1.019857"
FALSE 16 "[23]\ttrain-rmse:0.369208\tval-rmse:1.024546"
FALSE 17 "[10]\ttrain-rmse:0.504929\tval-rmse:1.025554"
FALSE 18 "[15]\ttrain-rmse:0.190793\tval-rmse:1.026910"
FALSE 19 "[11]\ttrain-rmse:0.357269\tval-rmse:1.029359"
FALSE 20 "[46]\ttrain-rmse:0.262580\tval-rmse:1.031247"
mod_summary_5d_71m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[12]\ttrain-rmse:0.418125\tval-rmse:0.959395"
FALSE 2 "[21]\ttrain-rmse:0.195162\tval-rmse:1.005950"
FALSE 3 "[22]\ttrain-rmse:0.307149\tval-rmse:1.009616"
FALSE 4 "[35]\ttrain-rmse:0.296614\tval-rmse:1.012367"
FALSE 5 "[10]\ttrain-rmse:0.439556\tval-rmse:1.022235"
FALSE 6 "[18]\ttrain-rmse:0.358194\tval-rmse:1.036492"
FALSE 7 "[37]\ttrain-rmse:0.217485\tval-rmse:1.053069"
FALSE 8 "[14]\ttrain-rmse:0.400128\tval-rmse:1.057614"
FALSE 9 "[73]\ttrain-rmse:0.226827\tval-rmse:1.057913"
FALSE 10 "[39]\ttrain-rmse:0.424131\tval-rmse:1.057921"
FALSE 11 "[9]\ttrain-rmse:0.257221\tval-rmse:1.060352"
FALSE 12 "[24]\ttrain-rmse:0.322158\tval-rmse:1.069728"
FALSE 13 "[9]\ttrain-rmse:0.485880\tval-rmse:1.071495"
FALSE 14 "[29]\ttrain-rmse:0.128647\tval-rmse:1.071771"
FALSE 15 "[28]\ttrain-rmse:0.182173\tval-rmse:1.073599"
FALSE 16 "[31]\ttrain-rmse:0.448011\tval-rmse:1.074643"
FALSE 17 "[14]\ttrain-rmse:0.261694\tval-rmse:1.077375"
FALSE 18 "[40]\ttrain-rmse:0.401865\tval-rmse:1.077975"
FALSE 19 "[38]\ttrain-rmse:0.332829\tval-rmse:1.077997"
FALSE 20 "[36]\ttrain-rmse:0.460572\tval-rmse:1.078719"
# most of best models are overfit, so looking for train/test RMSE that are closer
optimized_booster_5d_3m <- mod_summary_5d_3m$mod[4][[1]] # this one is quite overfit - train rmse is 0.5, test is 1
optimized_booster_5d_5m <- mod_summary_5d_5m$mod[2][[1]] # all these options are pretty overfit.
optimized_booster_5d_51m <- mod_summary_5d_51m$mod[3][[1]] # still overfit
optimized_booster_5d_71m <- mod_summary_5d_71m$mod[1][[1]] # again, pretty overfit
# Apply best mod
preds_5 <- val_5 %>%
mutate(pred_secchi_5d_5m = predict(optimized_booster_5d_5m, dval_5d_5m),
pred_secchi_5d_3m = predict(optimized_booster_5d_3m, dval_5d_3m),
pred_secchi_5d_71m = predict(optimized_booster_5d_71m, dval_5d_71m),
pred_secchi_5d_51m = predict(optimized_booster_5d_51m, dval_5d_51m))
evals_5 <- preds_5 %>%
summarise(across(c(pred_secchi_5d_5m, pred_secchi_5d_3m, pred_secchi_5d_71m, pred_secchi_5d_51m),
list(rmse = ~rmse(secchi, .),
mae = ~mae(secchi, .),
mape = ~mape(secchi, .),
bias = ~bias(secchi, .),
p.bias = ~percent_bias(secchi, .),
smape = ~smape(secchi, .),
r2 = ~cor(secchi, .)^2),
.names = "{fn}_{col}"))
evals_5
FALSE rmse_pred_secchi_5d_5m mae_pred_secchi_5d_5m mape_pred_secchi_5d_5m
FALSE 1 0.8916389 0.7046072 0.1942366
FALSE bias_pred_secchi_5d_5m p.bias_pred_secchi_5d_5m smape_pred_secchi_5d_5m
FALSE 1 0.2590617 0.03325661 0.2014614
FALSE r2_pred_secchi_5d_5m rmse_pred_secchi_5d_3m mae_pred_secchi_5d_3m
FALSE 1 0.4403616 1.038295 0.8179092
FALSE mape_pred_secchi_5d_3m bias_pred_secchi_5d_3m p.bias_pred_secchi_5d_3m
FALSE 1 0.2282917 0.3925272 0.06173814
FALSE smape_pred_secchi_5d_3m r2_pred_secchi_5d_3m rmse_pred_secchi_5d_71m
FALSE 1 0.2424634 0.3195622 0.9297341
FALSE mae_pred_secchi_5d_71m mape_pred_secchi_5d_71m bias_pred_secchi_5d_71m
FALSE 1 0.7264634 0.2040276 0.3166904
FALSE p.bias_pred_secchi_5d_71m smape_pred_secchi_5d_71m r2_pred_secchi_5d_71m
FALSE 1 0.05325865 0.2155079 0.4374517
FALSE rmse_pred_secchi_5d_51m mae_pred_secchi_5d_51m mape_pred_secchi_5d_51m
FALSE 1 0.9191537 0.7264615 0.2020312
FALSE bias_pred_secchi_5d_51m p.bias_pred_secchi_5d_51m smape_pred_secchi_5d_51m
FALSE 1 0.2477498 0.03405154 0.2099985
FALSE r2_pred_secchi_5d_51m
FALSE 1 0.4315663
mod_summary_jd_3m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[5]\ttrain-rmse:1.053405\tval-rmse:0.732145"
FALSE 2 "[14]\ttrain-rmse:0.601904\tval-rmse:0.734536"
FALSE 3 "[21]\ttrain-rmse:0.649525\tval-rmse:0.735325"
FALSE 4 "[22]\ttrain-rmse:0.758808\tval-rmse:0.739086"
FALSE 5 "[15]\ttrain-rmse:0.529075\tval-rmse:0.740712"
FALSE 6 "[21]\ttrain-rmse:0.679336\tval-rmse:0.741152"
FALSE 7 "[22]\ttrain-rmse:0.616632\tval-rmse:0.741287"
FALSE 8 "[19]\ttrain-rmse:0.752240\tval-rmse:0.743072"
FALSE 9 "[21]\ttrain-rmse:0.781823\tval-rmse:0.745712"
FALSE 10 "[38]\ttrain-rmse:0.508722\tval-rmse:0.746619"
FALSE 11 "[24]\ttrain-rmse:0.618787\tval-rmse:0.746728"
FALSE 12 "[25]\ttrain-rmse:0.487934\tval-rmse:0.748918"
FALSE 13 "[21]\ttrain-rmse:0.685765\tval-rmse:0.750516"
FALSE 14 "[22]\ttrain-rmse:0.651607\tval-rmse:0.750522"
FALSE 15 "[23]\ttrain-rmse:0.574940\tval-rmse:0.751599"
FALSE 16 "[9]\ttrain-rmse:0.697727\tval-rmse:0.752579"
FALSE 17 "[23]\ttrain-rmse:0.570996\tval-rmse:0.752864"
FALSE 18 "[8]\ttrain-rmse:0.665623\tval-rmse:0.753821"
FALSE 19 "[21]\ttrain-rmse:0.685295\tval-rmse:0.754233"
FALSE 20 "[23]\ttrain-rmse:0.627481\tval-rmse:0.756205"
mod_summary_jd_5m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[24]\ttrain-rmse:0.589641\tval-rmse:0.775547"
FALSE 2 "[7]\ttrain-rmse:0.641924\tval-rmse:0.791019"
FALSE 3 "[5]\ttrain-rmse:0.875744\tval-rmse:0.795134"
FALSE 4 "[8]\ttrain-rmse:0.579254\tval-rmse:0.798143"
FALSE 5 "[6]\ttrain-rmse:0.688624\tval-rmse:0.802263"
FALSE 6 "[22]\ttrain-rmse:0.753772\tval-rmse:0.802359"
FALSE 7 "[20]\ttrain-rmse:0.676896\tval-rmse:0.807530"
FALSE 8 "[7]\ttrain-rmse:0.813274\tval-rmse:0.810102"
FALSE 9 "[22]\ttrain-rmse:0.753550\tval-rmse:0.811635"
FALSE 10 "[21]\ttrain-rmse:0.727240\tval-rmse:0.813047"
FALSE 11 "[21]\ttrain-rmse:0.842255\tval-rmse:0.813709"
FALSE 12 "[19]\ttrain-rmse:0.913022\tval-rmse:0.814985"
FALSE 13 "[22]\ttrain-rmse:0.698649\tval-rmse:0.816389"
FALSE 14 "[22]\ttrain-rmse:0.746893\tval-rmse:0.818785"
FALSE 15 "[25]\ttrain-rmse:0.730175\tval-rmse:0.820589"
FALSE 16 "[22]\ttrain-rmse:0.598218\tval-rmse:0.821636"
FALSE 17 "[19]\ttrain-rmse:0.776063\tval-rmse:0.822102"
FALSE 18 "[7]\ttrain-rmse:0.795072\tval-rmse:0.822194"
FALSE 19 "[23]\ttrain-rmse:0.671616\tval-rmse:0.823878"
FALSE 20 "[23]\ttrain-rmse:0.754198\tval-rmse:0.824009"
mod_summary_jd_51m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[11]\ttrain-rmse:0.563328\tval-rmse:0.719620"
FALSE 2 "[20]\ttrain-rmse:0.640292\tval-rmse:0.732055"
FALSE 3 "[9]\ttrain-rmse:0.704207\tval-rmse:0.739378"
FALSE 4 "[7]\ttrain-rmse:0.578080\tval-rmse:0.748831"
FALSE 5 "[31]\ttrain-rmse:0.610108\tval-rmse:0.757068"
FALSE 6 "[8]\ttrain-rmse:0.594283\tval-rmse:0.765976"
FALSE 7 "[21]\ttrain-rmse:0.594361\tval-rmse:0.771174"
FALSE 8 "[8]\ttrain-rmse:0.488876\tval-rmse:0.777529"
FALSE 9 "[6]\ttrain-rmse:0.695668\tval-rmse:0.777999"
FALSE 10 "[6]\ttrain-rmse:0.641776\tval-rmse:0.783015"
FALSE 11 "[8]\ttrain-rmse:0.723066\tval-rmse:0.783788"
FALSE 12 "[31]\ttrain-rmse:0.592202\tval-rmse:0.787315"
FALSE 13 "[22]\ttrain-rmse:0.763185\tval-rmse:0.792019"
FALSE 14 "[25]\ttrain-rmse:0.273874\tval-rmse:0.797882"
FALSE 15 "[19]\ttrain-rmse:0.674177\tval-rmse:0.798889"
FALSE 16 "[27]\ttrain-rmse:0.737569\tval-rmse:0.799222"
FALSE 17 "[25]\ttrain-rmse:0.518851\tval-rmse:0.799871"
FALSE 18 "[7]\ttrain-rmse:0.538728\tval-rmse:0.801856"
FALSE 19 "[19]\ttrain-rmse:0.672952\tval-rmse:0.804626"
FALSE 20 "[25]\ttrain-rmse:0.714120\tval-rmse:0.806391"
mod_summary_jd_71m %>% select(best_message)
FALSE # A tibble: 20 × 1
FALSE best_message
FALSE <chr>
FALSE 1 "[25]\ttrain-rmse:0.706391\tval-rmse:0.688101"
FALSE 2 "[22]\ttrain-rmse:0.621843\tval-rmse:0.711899"
FALSE 3 "[24]\ttrain-rmse:0.541285\tval-rmse:0.722433"
FALSE 4 "[8]\ttrain-rmse:0.622469\tval-rmse:0.723832"
FALSE 5 "[22]\ttrain-rmse:0.582793\tval-rmse:0.732713"
FALSE 6 "[37]\ttrain-rmse:0.494461\tval-rmse:0.733796"
FALSE 7 "[22]\ttrain-rmse:0.577833\tval-rmse:0.736067"
FALSE 8 "[22]\ttrain-rmse:0.589941\tval-rmse:0.739976"
FALSE 9 "[25]\ttrain-rmse:0.528916\tval-rmse:0.740118"
FALSE 10 "[7]\ttrain-rmse:0.598981\tval-rmse:0.748153"
FALSE 11 "[31]\ttrain-rmse:0.353413\tval-rmse:0.749748"
FALSE 12 "[6]\ttrain-rmse:0.671189\tval-rmse:0.749940"
FALSE 13 "[27]\ttrain-rmse:0.505670\tval-rmse:0.751821"
FALSE 14 "[21]\ttrain-rmse:0.600679\tval-rmse:0.752852"
FALSE 15 "[26]\ttrain-rmse:0.450615\tval-rmse:0.754393"
FALSE 16 "[24]\ttrain-rmse:0.554773\tval-rmse:0.756088"
FALSE 17 "[20]\ttrain-rmse:0.636889\tval-rmse:0.758873"
FALSE 18 "[22]\ttrain-rmse:0.590224\tval-rmse:0.759143"
FALSE 19 "[21]\ttrain-rmse:0.633044\tval-rmse:0.760393"
FALSE 20 "[23]\ttrain-rmse:0.591821\tval-rmse:0.760800"
# best mod looks good here across the board
optimized_booster_jd_3m <- mod_summary_jd_3m$mod[1][[1]]
optimized_booster_jd_5m <- mod_summary_jd_5m$mod[1][[1]]
optimized_booster_jd_51m <- mod_summary_jd_51m$mod[1][[1]]
optimized_booster_jd_71m <- mod_summary_jd_71m$mod[1][[1]]
# Apply best mod
preds_jd <- val_j %>%
mutate(pred_secchi_jd_5m = predict(optimized_booster_jd_5m, dval_jd_5m),
pred_secchi_jd_3m = predict(optimized_booster_jd_3m, dval_jd_3m),
pred_secchi_jd_51m = predict(optimized_booster_jd_51m, dval_jd_51m),
pred_secchi_jd_71m = predict(optimized_booster_jd_71m, dval_jd_71m))
evals_jd <- preds_jd %>%
summarise(across(c(pred_secchi_jd_5m, pred_secchi_jd_3m, pred_secchi_jd_71m, pred_secchi_jd_51m),
list(rmse = ~rmse(secchi, .),
mae = ~mae(secchi, .),
mape = ~mape(secchi, .),
bias = ~bias(secchi, .),
p.bias = ~percent_bias(secchi, .),
smape = ~smape(secchi, .),
r2 = ~cor(secchi, .)^2),
.names = "{fn}_{col}"))
evals_jd
FALSE rmse_pred_secchi_jd_5m mae_pred_secchi_jd_5m mape_pred_secchi_jd_5m
FALSE 1 0.7238251 0.6132381 0.1980581
FALSE bias_pred_secchi_jd_5m p.bias_pred_secchi_jd_5m smape_pred_secchi_jd_5m
FALSE 1 0.1221853 -0.0223849 0.1909397
FALSE r2_pred_secchi_jd_5m rmse_pred_secchi_jd_3m mae_pred_secchi_jd_3m
FALSE 1 0.6242054 0.904584 0.7361402
FALSE mape_pred_secchi_jd_3m bias_pred_secchi_jd_3m p.bias_pred_secchi_jd_3m
FALSE 1 0.2167459 0.3849171 0.05141815
FALSE smape_pred_secchi_jd_3m r2_pred_secchi_jd_3m rmse_pred_secchi_jd_71m
FALSE 1 0.2253142 0.506181 0.7583794
FALSE mae_pred_secchi_jd_71m mape_pred_secchi_jd_71m bias_pred_secchi_jd_71m
FALSE 1 0.634782 0.2111927 0.1133909
FALSE p.bias_pred_secchi_jd_71m smape_pred_secchi_jd_71m r2_pred_secchi_jd_71m
FALSE 1 -0.03120633 0.2017568 0.5747047
FALSE rmse_pred_secchi_jd_51m mae_pred_secchi_jd_51m mape_pred_secchi_jd_51m
FALSE 1 0.8313475 0.694705 0.2483355
FALSE bias_pred_secchi_jd_51m p.bias_pred_secchi_jd_51m smape_pred_secchi_jd_51m
FALSE 1 -0.1823586 -0.1207984 0.2188396
FALSE r2_pred_secchi_jd_51m
FALSE 1 0.4852026
Keep in mind that all of these models seemed overfit in the train/test sets.
Considering that the train/test metrics seemed overfit, the validation look pretty good here.
By looking at the train/test results, the validation results (especially the model performance at higher Secchi estimates), and the overall r^2 of the validation, it looks like the 7/1 day window with the 5/1 day met data is the best performing. This is a relatively subjective decision - and the end user can absolutely make a different decision.
features = band_met51_feats
model = optimized_booster_jd_51m
met = '5/1 day met summaries'
window = '7/1 day window'
save(optimized_booster_jd_51m, file = 'data/models/optimized_xg_8_jd_51m.RData')
full_stack <- read_csv('data/upstreamRS/yojoa_corr_rrs_met_scaled_v2023-06-15.csv') %>%
mutate(secchi = 100) %>%
prepData(.) %>%
filter(date < ymd('2023-01-01'))
stack_xgb <- xgb.DMatrix(data = as.matrix(full_stack[,features]))
full_stack_simp <- full_stack %>%
mutate(secchi = predict(model, stack_xgb)) %>%
select(date, location, secchi, mission)
situ_stack <- read_csv('data/in-situ/Secchi_completedataset.csv') %>%
mutate(secchi = as.numeric(secchi),
date = mdy(date)) %>%
filter(!is.na(secchi)) %>%
mutate(mission = 'Measured') %>%
bind_rows(full_stack_simp)%>%
mutate(location = gsub(' ', '', location))
Let’s look at each of the site records alongside the Landsat-estimated Secchi depth.
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plotRecentBySite = function(site) {
ggplot(situ_stack %>%
filter(location == site), aes(x = date, y = secchi, color = mission,
shape = mission)) +
geom_point() +
labs(title = paste0('Yojoa Secchi 2018-2022 - Site ', site),
subtitle = paste0(window, ', ', met, ', moderate data stringency'),
y = 'Secchi (m)',
color = 'data source', shape = 'data source') +
scale_color_manual(values = c('grey10','grey30','grey50','grey70','blue')) +
theme_few() +
theme(legend.position = c(0.8,0.8)) +
scale_shape_manual(values = c(19,19,19,19,1)) +
scale_y_continuous(limits = c(0, max(situ_stack$secchi)), breaks = seq(0, max(situ_stack$secchi), 2)) +
scale_x_date(limits = c(ymd('2018-01-01'), max(situ_stack$date))) +
theme(plot.title = element_text(hjust = 0.5, face = 'bold'),
plot.subtitle = element_text(hjust = 0.5))
}
map(sort(unique(situ_stack$location)), plotRecentBySite)
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While there is plenty of variability across the lake, let’s summarize to a single value per date, since not all sites have the same density of record. Since there are a few oddballs in here (both in terms of measured and estimated), we’ll use the median Secchi across all sites.
lake_med <- situ_stack %>%
group_by(date,mission) %>%
summarize(across(where(is.numeric),median))
ggplot(lake_med, aes(x = date, y = secchi, color = mission, shape = mission)) +
geom_point() +
scale_color_manual(values = c('grey10','grey30','grey50','grey70','blue')) +
theme_few() +
labs(title = 'Yojoa Secchi 2018-2022\nwhole-lake median',
subtitle = paste0(window, ', ', met, ', moderate data stringency'),
y = 'median Secchi (m)',
color = 'data source', shape = 'data source') +
theme(legend.position = c(0.8,0.8)) +
scale_shape_manual(values = c(19,19,19,19,1)) +
scale_x_date(limits = c(as.Date('2018-01-01'), as.Date('2023-01-01')))+
theme(plot.title = element_text(hjust = 0.5, face = 'bold'),
plot.subtitle = element_text(hjust = 0.5))
feat_importance = xgb.importance(feature_names = band_met51_feats,
model = optimized_booster_jd_51m)
xgb.plot.importance(feat_importance)